A population-based study of rates of childbirth in recurrence-free female young adult survivors of Non-gynecologic malignancies
Bibliographic record
Abstract
BACKGROUND: Fertility is an important issue for long-term survivors of malignancies developing during reproductive years. We designed a population-based study to investigate childbirth in female young adult survivors of non-gynecologic malignancies. METHODS: Women 20-34 years diagnosed with non-gynecologic malignancies in Ontario from 1992-1999 who lived at least 5 years recurrence-free were identified using the Ontario Cancer Registry and age matched to 5 randomly selected cancer-free women. Childbirth was determined through hospital discharge data. Time-to-childbirth was compared between survivors and controls using Cox proportional hazard regression for all subjects and stratified by prior childbirth and disease site. RESULTS: 3,285 survivors and 15,118 control women had a median of 12 years observation. 1,194 survivors and 6,049 controls experienced childbirth to the end of observation (March 2011). Overall, survivors experienced a longer time to childbirth than controls (HR 0.92, 95% CI 0.87-0.98), however this was limited to survivors with prediagnosis childbirth (HR 0.76, 95% CI 0.66-0.86). Survivors with no prediagnosis childbirth experienced a similar time to childbirth (HR 1.00, 95% CI 0.93-1.08) as control women. Differences between survivors and controls varied by type of malignancy; notably for those with prediagnosis childbirth, survivors of breast cancer (HR 0.45, 95% CI 0.29-0.68) and Hodgkin Disease (HR 0.57, 95% CI 0.36-0.91) had lower rates of postdiagnosis childbirth than controls. CONCLUSIONS: Long-term female young adult survivors of malignancies are less likely than controls to have childbirth after diagnosis; the overall effect is small and is influenced by prediagnosis childbirth and malignancy type.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".